In 3D-TV, 3D-video and 3D-cinema, information of two or even more images is joined together for production of a spatial reproduction of image content. Typically, two stereoscopic images are used for computation of depth information, wherein a matching process is applied to find point correspondences in the two input or basic images. The displacement between two corresponding points in the basic images resulting from the different positions of the cameras when capturing the real world scene is commonly referred to as disparity. A 3D-structure, i.e. the depth information of the captured scene, may be reconstructed from these disparities by triangulation if the camera parameters are known. Depth information for the pixels in the basic images is usually integrated into a disparity map containing the result of the respective matching calculations.
The performance of the stereo matching process inherently depends on the underlying image content. Even for ideal conditions there still remain several problems, e.g. occluded areas in one of the input pictures, perspective deformations due to lens distortions, specular reflections or missing texture in some region of the image, etc., that make the matching process a challenging task. For some parts of an image it is inherently more difficult to determine accurate values for the disparity, also referred to as disparity estimates, than for others. This leads to varying levels of accuracy and reliability for the disparity estimates.
For some applications, e.g. for subtitling or positioning of graphical overlays, it is beneficial to select a reliable or even highly reliable subset of disparity estimates from a dense disparity map in order to create a reliable or highly reliable sparse disparity map. Moreover, for post-production purposes it is beneficial to accurately mark problematic and non-problematic regions to process them with special algorithms etc.
The above can be accomplished with a confidence evaluation, which determines the reliability of a disparity estimate to evaluate whether it is an accurate point correspondence or not. To this end the confidence evaluation provides a certain level of selectivity. An increased selectivity of the confidence evaluation leads to a higher share of accurate point correspondences at the cost of a reduced coverage. Ideally, the share of accurate point correspondences is close to 100% for the highest confidence values or an interval comprising only the highest confidence values and then it slowly decreases for lower confidence values with a high concentration of the remaining inaccurate point correspondences at the confidence of 0.